Tag Archives: customer journey measurement

Data collection technology is at the heart of in-store customer location analytics. In my past two posts, I’ve described some of the cool analytics and measurement that our second release of DM1 brings to the enterprise. And in a way, this is the only stuff that matters. It’s what you use to solve problems. But you can’t solve those problems and DM1 can’t give you the measurement you need, without a workable data collection technology: a technology that’s reliable, accurate, and cost-effective to deploy. In digital analytics, tagging was that technology. And while tagging can seem mysterious, a basic tag is really nothing more than 20 lines of javascript code that any competent programmer could write in a day. For in-store location analytics, it’s a lot more challenging. It’s so challenging, in fact, that we’ve struggled to find data collection technologies that meet our needs. We’ve engineered the DM1 platform to be hardware and collection neutral. We take data from a variety of sources and we’ll engineer the best possible measurement from that source. But we do have a favorite – and it’s a solution that’s become our go to suggestion for MOST clients. It’s called iViu.

The iViu technology uses passive network sniffers. These little devices track smart-phones (and potentially other electronics like Smart Watches). They triangulate on the signal the device sends out to position the phone. And because they can identify the phone, they are able to track the full shopper journey from just outside the store to cash-wrap or exit. Like almost all in-store tracking devices, they can’t identify who somebody is, though they can track the same device over time. So the iViu data does let us track same-store usage (at least outside of the EU where, to be fully compliant with EU guidelines we throw away device signatures at the end of each day) and even the same shopper at different locations under your real estate portfolio. But it doesn’t tell us who the shopper is or give us a natural join key to household or digital data.

In laying out the basics, I’ve glossed over all the complexity involved – and there’s a lot. In store location analytics, data collection technologies compete along several critical dimensions: coverage, accuracy, journey measurement, demographics, privacy, associate tracking, ease of implementation and cost. Each technology and each vendor has its own unique strengths. I’m going to cover each of these factors, explain where iViu fits in, and summarize why we usually end up choosing their technology. The three main location analytics technology contenders are Camera, Passive Network Sniffers (iViu) and off the shelf WiFi access points.

Population Coverage: Ideally, a counting system will measure every shopper who comes in the store. If a counting system doesn’t collect everyone, it’s important to understand the breadth of its coverage and whether it introduces any deep bias into the measurement. In terms of population coverage, you can’t beat camera systems. They are the best technology around for getting 100% coverage. They rarely miss anyone and if anything, their pitfall is that they can be prone to overcounting. All electronic mechanisms are limited to tracking shoppers with smart-phones. In the U.S. (and most of the world), that isn’t much of a problem. It does mean you likely won’t be counting smaller kids. Of more significance, however, is that the phone must be an emitter – with either its Bluetooth or WiFi turned on. Best estimates are that about 15% of people don’t enable those signals. So an iViu device will typically get signals from about 80-85% of the population. And we think that’s a relatively unbiased group – probably a more accurate sample than what we get in the digital world using cookies. One big advantage to the iViu system versus other electronic systems is that iViu does a much better job tracking iPhones or other devices that employ MAC randomization. Why are iPhones an issue? Beginning with iOS8, Apple started randomizing the MAC address of the device when it pings out to the world. WiFi access points and most electronic detectors use the MAC address as the device identifier. So every time an iOS device pings a typical collector, it will look like a different device. In practice, this means that WiFi based coverage will lose all iOS devices that aren’t connected to your network. That’s a pretty huge problem. In addition, iViu devices are dedicated to measurement and they do a much better job of listening than standard WiFi access points. In side by side tests, iViu devices pick up more shoppers, more consistently.

So for Population Coverage, we see it this way:

Camera: Best

iViu: Good

WiFi: Poor

Accuracy: There are lots of different ways to think about accuracy and many different use-cases and data quality problems. I’m going to focus here on basic positional accuracy – the ability to locate the shopper at particular place in the store. Positional accuracy isn’t vital for applications like door-counting – but our DM1 platform absolutely depends on it. We map location to the store and report and segment based on shopper interest. If the mapped location is wrong, our analytics are wrong. With camera systems, each camera covers a specific area of the store and it’s relatively easy to map the location of the person to the specific part of the area the camera covers. For electronic tracking, it’s more complicated. Most electronic systems work by using either (or both) the relative signal strength detected and a triangulation of the signal across devices. But while the methods used are similar, the end result is quite different depending on the implementation and the vendor. Using the iViu devices, we can usually get an accuracy of location down to about 1.5 meters. That’s almost always good enough for the type of measurement we do with DM1. With off-the-shelf WiFi systems, the accuracy is more like 10 meters (and that’s often best case). We can live with that level of accuracy if we’re doing measurement on a mall, stadium, airport or a resort. But for a store, it just isn’t good enough to work with.

So for accuracy we see it this way:

Camera: Good

iViu: Good

WiFi: Poor

Journey Measurement: All the interesting questions in shopper and store optimization involve the journey – the ability to track the shopper visit across the store. It’s fundamental to DM1 and big part of what our analytics bring to the table. In theory, all the data collection technologies should be able to track the journey. In practice, however, we’ve found that electronic systems do this vastly better than the current crop of camera systems. Electronic systems have the fairly easy task of distinguishing one phone from another. Camera systems have to track people. And while there have been dramatic improvements in facial recognition, those improvements are challenged by real-world measurement situations and often haven’t found their way into workable/available technology. A typical camera only covers a 20×20 foot area. So even a modest mall store will require a goodly number of cameras to track its full footprint. As shoppers move from zone-to-zone, the system has to be able to determine that it’s the same person. Camera systems suck at this. Suppose a camera system gets a zone crossing right 90% of the time. That sounds pretty good, until you realize that you have about a 50% chance of following a shopper across 100 feet of your store. It’s because of this limitation that so many camera-based systems are, essentially, zone counters. They count the shoppers in an area and their linger time. They don’t count journeys. It’s not a software problem. It’s a collection problem.

Camera: Poor

iViu: Good

WiFi: Good

Demographics: We’re behavioral analysts, but while we tend to believe that real behavior trumps demographics, it doesn’t mean we think demographics don’t matter. Age and gender are nearly always interesting analytic variables and it’s a distinct advantage to be able to collect them. The scorecard here is simple. Camera does a pretty good job of this. No electronic system does this at all.

Camera: Good

iViu: No

WiFi: No

Privacy: There’s an undeniable creep factor involved with in-store tracking and it can be a legitimate barrier to measurement. All of the technologies involved here do essentially the same thing and all of them do it anonymously. Some of our clients have preferred video to electronic measurement on privacy grounds. I frankly don’t understand that thinking, but privacy is an area where the arguments turn more on perception than reality. Both technologies are providing the same basic measurement and the only significant difference is that electronic measurement provides an opt-out mechanism and video doesn’t. For electronic measurement, there’s a national, online opt-out registry (and, of course, you can always turn off phone WiFi too). There is no equivalent system to opt-out of video measurement and if there was, I imagine it would involve sending in your face – which kind of sucks. I do think video benefits from the fact that most stores have already deployed it for security purposes (though the camera’s you use are usually different), but it’s hard to understand why it’s better to measure people one way than another when the implications for them are identical. I’m going to call this one a wash.

Camera: Ok

iViu: Ok

WiFi: Ok

Associate Tracking: Coming from the digital world, our focus when we started Digital Mortar was all about shoppers. But we quickly realized that tracking associates was critical. First, because associates are a huge part of the customer experience. You can’t really measure shopper journeys unless you can measure when and if they talked to an associate. But there’s also real value in understanding whether you had enough staff on the floor. If they were in the right places. If the type of associate, their training or experience or tactics, made a substantial performance difference. With electronic tracking, it’s pretty easy to measure associates (they just have to carry a device). In most cases, you don’t even have to register that device. DM1 automatically detects devices with employee behaviors and classifies them appropriately. We do that to minimize compliance issues. With camera, it’s a different story. Camera systems either conflate employees with shoppers (which is a disaster) or use supplementary electronic means to remove them from the data. Not only does this introduce complexity into the system, it makes it much harder to track and measure interactions. We’ve also seen minor compliance issues (a few associates forgetting to pick up their tags occasionally) have significant negative implications on measurement quality. It’s worth mentioning here that if you only want to measure associates, there are other technologies worth considering that require code on a mobile device but which will provide VERY accurate and detailed associate tracking.

Camera: Poor

iViu: Good

WiFi: Fair

Ease of Implementation: Let’s face it, having to put hardware into stores is a hassle. One of the big benefits to WiFi based measurement is that it can take advantage of existing access points that were put there to provision WiFi to customers or to support store functions. Every other form of measurement takes new hardware and store installation. But there is a pretty big difference in the level of effort required. It takes a lot of cameras to cover a store. The cameras have to be in the ceiling and they have to precisely placed. It’s real work to get right and it’s often expensive, involves some degree of retrofitting and is time consuming. An iViu device will cover something like 10x the area of a camera – so you need a lot less of them. It’s easier to install. It doesn’t have to precisely placed and it doesn’t have to ceiling mounted. We’ve put iViu devices under tables, on top of or behind displays, on pillars and even in drawers. They do require power (plug or PoE), but they’re a snap to setup – it’s pretty much plug and play – and we’ve found that we can install in most locations without huge difficulty.

Camera: Poor

iViu: Fair

WiFi: Good

Cost: You know how athletes who sign huge new contracts always say “It wasn’t about the money” and you’re thinking – “Of course it was about the money”? Money matters. Realistically, a store can only afford to spend so much on measurement. Worse, the more you sink into the hardware, the less you can spend on the stuff that actually makes a difference – the analytics software and the people to drive it. At Digital Mortar, we’re believers in comprehensive measurement. We want to measure every store – not one store out of a hundred. And if you’re trying to measure Associates, optimize locally, or do real-time interactions, measuring a single store just doesn’t cut it. So having a measurement technology that’s cheap enough to go fleet-wide? We think that’s priceless. From that perspective, you can’t beat WiFi since it’s usually already in place and even if you have to provision it, the cost is reasonable and there are extra benefits. But, as noted, there’s pretty limited analytics you can do with a 10 meter margin of error. For a system that provides robust measurement, we like the fact that iViu devices are very cost-effective. The hardware for most stores costs less than $5k (that’s to buy the devices not an annual cost). Even very, very large stores will cost less than $20K per store. Camera systems are often far more costly – to the point that they are generally impractical for very large stores and make deploying to a large number of stores impossible.

Camera: Poor

iViu: Good

WiFi: Very Good

As you can see, there isn’t one solution that’s perfect in every respect. And in the real world, we often find reasons to deploy each technology. In Part 2 of this post, I’ll summarize the findings, explain why – given it’s overall profile – iViu makes sense for most retail stores, and also talk a little bit about the ways that you can blend technologies to get the best of each world.

People have struggled with this (big) data provider model but Factual feels like it’s found a real (and valuable) niche. Would love to see more of this grow since external data is a huge miss in most big data systems.

Targeted VoC is a powerful (and totally neglected) tool for personalization. Facebook’s experience is entirely relevant to ANY content producer. I don’t know if I can take credit for this, but I suggested this to folks at Facebook a couple of years back!

An interesting discussion of the problems in identifying “likely” voters and the benefits of behavioral data integration. Food for thought in the enterprise world as well where the equivalent is often possible but rarely done.